Predictive Maintenance and Scheduling for a UK Manufacturer

Business Context

The manufacturer operated mixed-age equipment with variable telemetry quality and maintenance records spread across multiple systems. Engineering teams spent significant time firefighting urgent issues rather than executing planned maintenance programmes.

Core Challenges

1. Failure unpredictability: Early warning signals were present but not operationally actionable.
2. Planning disconnect: Maintenance schedules and production plans were not consistently synchronised.
3. Data quality variance: Sensor and work-order data required cleansing before modelling.

Delivery Approach

We delivered a pilot stack aligned to live operations:

1. Signal foundation: Unified telemetry and historical fault records with data quality checks.
2. Failure prediction layer: Risk scoring for key components with maintenance-priority thresholds.
3. Workflow automation layer: Automatic work-order triggers, escalation paths, and schedule notifications.
4. Operations dashboard: Shift-level view of asset risk, pending interventions, and completed actions.

Implementation Timeline

• Weeks 1-2: Equipment selection, fault taxonomy, and baseline KPI capture.
• Weeks 3-5: Data pipeline build, quality controls, and model prototyping.
• Weeks 6-8: Maintenance workflow automation and dashboard deployment.
• Weeks 9-10: Pilot stabilisation, operator training, and review cadence setup.

Operational Outcomes

Maintenance teams reduced emergency callouts and improved intervention timing. Production managers gained earlier risk visibility and clearer decision support for balancing uptime, quality, and schedule commitments.

Next-Phase Roadmap

The business is now scaling the model to additional plants and introducing spare-parts forecasting to further reduce avoidable delays.